Spyros Skiadopoulos

Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications

The current study investigates texture properties of the tissue surrounding microcalcification (MC) clusters on mammograms for breast cancer diagnosis. The case sample analyzed consists of 85 dense mammographic images, originating from the digital database for screening mammography. mammograms analyzed contain 100 subtle MC clusters (46 benign and 54 malignant). The tissue surrounding MCs is defined on original and wavelet decomposed images, based on a redundant discrete wavelet transform. Gray-level texture and wavelet coefficient texture features at three decomposition levels are extracted from surrounding tissue regions of interest (ST-ROIs). Specifically, gray-level first-order statistics, gray-level cooccurrence matrices features, and Lawspsila texture energy measures are extracted from original image ST-ROIs. Wavelet coefficient first-order statistics and wavelet coefficient cooccurrence matrices features are extracted from subimages ST-ROIs. The ability of each feature set in differentiating malignant from benign tissue is investigated using a probabilistic neural network. Classification outputs of most discriminating feature sets are combined using a majority voting rule. The proposed combined scheme achieved an area under receiver operating characteristic curve (Az) of 0.989. Results suggest that MCspsila ST texture analysis can contribute to computer-aided diagnosis of breast cancer.

Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis

The current study investigates the feasibility of using texture analysis to quantify the heterogeneity of lesion enhancement kinetics in order to discriminate malignant from benign breast lesions. A total of 82 biopsy-proven breast lesions (51 malignant, 31 benign), originating from 74 women subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were analysed. Pixel-wise analysis of DCE-MRI lesion data was performed to generate initial enhancement, post-initial enhancement and signal enhancement ratio (SER) parametric maps; these maps were subsequently subjected to co-occurrence matrix texture analysis. The discriminating ability of texture features extracted from each parametric map was investigated using a least-squares minimum distance classifier and further compared with the discriminating ability of the same texture features extracted from the first post-contrast frame. Selected texture features extracted from the SER map achieved an area under receiver operating characteristic curve of 0.922 +/- 0.029, a performance similar to post-initial enhancement map features (0.906 +/- 0.032) and statistically significantly higher than for initial enhancement map (0.767 +/- 0.053) and first post-contrast frame (0.756 +/- 0.060) features. Quantifying the heterogeneity of parametric maps that reflect lesion washout properties could contribute to the computer-aided diagnosis of breast lesions in DCE-MRI.

Texture classification‐based segmentation of lung affected by interstitial pneumonia in high‐resolution CT

Accurate and automated lung field (LF) segmentation in high-resolution computed tomography (HRCT) is highly challenged by the presence of pathologies affecting lung borders, also affecting the performance of computer-aided diagnosis (CAD) schemes. In this work, a two-dimensional LF segmentation algorithm adapted to interstitial pneumonia (IP) patterns is presented. The algorithm employs k-means clustering followed by a filling operation to obtain an initial LF order estimate. The final LF border is obtained by an iterative support vector machine neighborhood labeling of border pixels based on gray level and wavelet coefficient statistics features. A second feature set based on gray level averaging and gradient features was also investigated to evaluate its effect on segmentation performance of the proposed method. The proposed method is evaluated on a dataset of 22 HRCT cases spanning a range of IP patterns such as ground glass, reticular, and honeycombing. The accuracy of the method is assessed using area overlap and shape differentiation metrics (d(mean), d(rms), and d(max)), by comparing automatically derived lung borders to manually traced ones, and further compared to a gray level thresholding-based (GLT-based) method. Accuracy of the methods evaluated is also compared to interobserver variability. The proposed method incorporating gray level and wavelet coefficient statistics demonstrated the highest segmentation accuracy, averaged over left and right LFs (overlap=0.954, d(mean)=1.080 mm, d(rms)=1.407 mm, and d(max)=4.944 mm), which is statistically significant (two-tailed students t test for paired data, p<0.0083) with respect to all metrics considered as compared to the proposed method incorporating gray level averaging and gradient features (overlap=0.918, d(mean)=2.354 mm, d(rms)=3.711 mm, and d(max)=14.412 mm) and the GLT-based method (overlap=0.897, d(mean)=3.618 mm, d(rms)=5.007 mm, and d(max)=16.893 mm). The performance of the three segmentation methods, although decreased as IP pattern severity level (mild, moderate, and severe) was increased, did not demonstrate statistically significant difference (two-tailed students t test for unpaired data, p>0.0167 for all metrics considered). Finally, the accuracy of the proposed method, based on gray level and wavelet coefficient statistics ranges within interobserver variability. The proposed segmentation method could be used as an initial stage of a CAD scheme for IP patterns.

A digital equalisation technique improving visualisation of dense mammary gland and breast periphery in mammography.

INTRODUCTION In mammographic imaging, use of high contrast screen-film combinations results in under-exposed and over-exposed film areas corresponding to dense mammary gland and breast periphery (BP), respectively, characterised by degraded image contrast. A digital equalisation technique was designed and developed in order to deal with the problem of poor visualisation of these regions. METHODS AND MATERIAL The technique is based on the film-digitiser characteristic curve and a layer model of the breast region, as depicted on a mammogram. It remaps each layer grey level (GL) values by a correction factor that accounts for thickness variation in BP and the presence of dense fibroglandular tissues at the mammary gland. The major steps of the technique are segmentation, to isolate the breast region from mammogram background, and adaptive layer GL remapping. RESULTS The performance of the technique was initially evaluated on a sample of 60 mammograms. Comparative evaluation between the initial and processed images was performed on the basis of nine anatomical features situated at dense mammary gland and BP. The mammographic images resulting from application of the proposed technique are GL equalised and the visualisation improvement of all anatomical features was found to be statistically significant (P<0.05) or highly significant (P<0.0001). The proposed technique was also compared with contrast-limited adaptive histogram equalisation (CLAHE) and found to be more effective in the visualisation of all anatomical features examined, for both dense breast (DB) and BP. DISCUSSION AND CONCLUSION Application of the proposed technique results in improved visualisation of both dense mammary gland and BP regions. The proposed technique is independent of breast size, breast symmetry and mammographic view. The technique contributes to breast dose minimisation by eliminating the need for a second acquisition.

Simulating the mammographic appearance of circumscribed lesions

Abstract. Optimization performance of digital image post-processing techniques in mammography requires controlled conditions of data sets permitting quantitative representation of image characteristics of pathological findings. Digital test objects, although objective and quantitative, do not mimic mammographic appearance and clinical data sets do not provide adequate sets of values of the various pathological finding characteristics. This can be overcome by digital simulation of pathological findings and superimposition on mammographic images. A simple method for simulation of mammographic appearance of radiopaque and/or radiolucent circumscribed lesions is presented. Circumscribed lesions are simulated using grey-level transformation functions which shift and compress the range of the initial pixel grey-level values in a region of interest (ROI) of a digitized mammographic image, according to grey-level analysis in 200 ROIs of real circumscribed lesions from digitized mammographic images. Simulation addresses lesion image characteristics, such as elliptical shape, orientation, halo sign for radiopaque lesions and capsule for radiolucent lesions, and is implemented in a user-driven PC-based interactive application. The appearance of the lesions is evaluated by six radiologists on a sample of 60 real and 60 simulated radiopaque lesions with the use of receiver operating characteristic (ROC) analysis. The area under the ROC curve, pooling the responses of the observers, was 0.55±0.03 indicating no statistically significant difference between real and simulated lesions (p>0.05). The method adequately simulates the mammographic appearance of circumscribed lesions and could be used to generate circumscribed lesion data sets for performance evaluation of image processing techniques, as well as education purposes.

Automated 3D segmentation of lung fields in thin slice CT exploiting wavelet preprocessing

Lung segmentation is a necessary first step to computer analysis in lung CT. It is crucial to develop automated segmentation algorithms capable of dealing with the amount of data produced in thin slice multidetector CT and also to produce accurate border delineation in cases of high density pathologies affecting the lung border. In this study an automated method for lung segmentation of thin slice CT data is proposed. The method exploits the advantage of a wavelet preprocessing step in combination with the minimum error thresholding technique applied on volume histogram. Performance averaged over left and right lung volumes is in terms of: lung volume overlap 0.983 ±0.008, mean distance 0.770 ± 0.251 mm, rms distance 0.520 ± 0.008 mm and maximum distance differentiation 3.327 ± 1.637 mm. Results demonstrate an accurate method that could be used as a first step in computer lung analysis in CT.

Size-adapted microcalcification segmentation in mammography utilizing scale-space signatures

The purpose of this study is size-adapted segmentation of individual microcalcifications in mammography, based on microcalcification scale-space signature estimation, enabling robust scale selection for initialization of multiscale active contours. Segmentation accuracy was evaluated by the area overlap measure, by comparing the proposed method and two recently proposed ones to expert manual delineations. The method achieved area overlap of 0.61+/-0.15 outperforming statistically (p<0.001) the other two methods (0.53+/-0.18, 0.42+/-0.16). Only the proposed method performed equally for both small (< 460 microm) and large (>/= 460 microm) microcalcifications. Results indicate an accurate method, which could be utilized in computer-aided diagnosis schemes of microcalcification clusters.

Evaluating the effect of a wavelet enhancement method in characterization of simulated lesions embedded in dense breast parenchyma

Presence of dense parenchyma in mammographic images masks lesions resulting in either missed detections or mischaracterizations, thus decreasing mammographic sensitivity and specificity. The aim of this study is evaluating the effect of a wavelet enhancement method on dense parenchyma for a lesion contour characterization task, using simulated lesions. The method is recently introduced, based on a two-stage process, locally adaptive denoising by soft-thresholding and enhancement by linear stretching. Sixty simulated low-contrast lesions of known image characteristics were generated and embedded in dense breast areas of normal mammographic images selected from the DDSM database. Evaluation was carried out by an observer performance comparative study between the processed and initial images. The task for four radiologists was to classify each simulated lesion with respect to contour sharpness/unsharpness. ROC analysis was performed. Combining radiologists’ responses, values of the area under ROC curve (Az) were 0.93 (95% CI 0.89, 0.96) and 0.81 (CI 0.75, 0.86) for processed and initial images, respectively. This difference in Az values was statistically significant (Student’s t-test, P<0.05), indicating the effectiveness of the enhancement method. The specific wavelet enhancement method should be tested for lesion contour characterization tasks in softcopy-based mammographic display environment using naturally occurring pathological lesions and normal cases.

Evaluating image denoising methods in myocardial perfusion single photon emission computed tomography (SPECT) imaging

The statistical nature of single photon emission computed tomography (SPECT) imaging, due to the Poisson noise effect, results in the degradation of image quality, especially in the case of lesions of low signal-to-noise ratio (SNR). A variety of well-established single-scale denoising methods applied on projection raw images have been incorporated in SPECT imaging applications, while multi-scale denoising methods with promising performance have been proposed. In this paper, a comparative evaluation study is performed between a multi-scale platelet denoising method and the well-established Butterworth filter applied as a pre- and post-processing step on images reconstructed without and/or with attenuation correction. Quantitative evaluation was carried out employing (i) a cardiac phantom containing two different size cold defects, utilized in two experiments conducted to simulate conditions without and with photon attenuation from myocardial surrounding tissue and (ii) a pilot-verified clinical dataset of 15 patients with ischemic defects. Image noise, defect contrast, SNR and defect contrast-to-noise ratio (CNR) metrics were computed for both phantom and patient defects. In addition, an observer preference study was carried out for the clinical dataset, based on rankings from two nuclear medicine clinicians. Without photon attenuation conditions, denoising by platelet and Butterworth post-processing methods outperformed Butterworth pre-processing for large size defects, while for small size defects, as well as with photon attenuation conditions, all methods have demonstrated similar denoising performance. Under both attenuation conditions, the platelet method showed improved performance with respect to defect contrast, SNR and defect CNR in the case of images reconstructed without attenuation correction, however not statistically significant (p > 0.05). Quantitative as well as preference results obtained from clinical data showed similar performance of the denoising methods studied. In conclusion, the multi-scale platelet denoising method applied on raw projection images provides more efficient noise reduction while preserving image quality in a myocardial phantom SPECT imaging as compared to the Butterworth filter applied either on projection or reconstructed images. However, this trend in favour of the platelet denoising method was not observed on clinical data reconstructed either without or with attenuation correction.

Quantifying heterogeneity of lesion uptake in dynamic contrast enhanced MRI for breast cancer diagnosis

The current study investigates whether texture features extracted from lesion kinetics feature maps can be used for breast cancer diagnosis. Fifty five women with 57 breast lesions (27 benign, 30 malignant) were subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on 1.5T system. A linear-slope model was fitted pixel-wise to a representative lesion slice time series and fitted parameters were used to create three kinetic maps (wash out, time to peak enhancement and peak enhancement). 28 grey level co-occurrence matrices features were extracted from each lesion kinetic map. The ability of texture features per map in discriminating malignant from benign lesions was investigated using a Probabilistic Neural Network classifier. Additional classification was performed by combining classification outputs of most discriminating feature subsets from the three maps, via majority voting. The combined scheme outperformed classification based on individual maps achieving area under Receiver Operating Characteristics curve 0.960±0.029. Results suggest that heterogeneity of breast lesion kinetics, as quantified by texture analysis, may contribute to computer assisted tissue characterization in DCE-MRI.